Vision, Learning and Graphics group,
Dept. of Computer Science,
Courant Institute of Mathematical Sciences,
New York University
Director of Graduate Studies for
Master of Science in Data Science
My research is in the areas of Machine Learning and Computer Vision. I am particularly interested in applying Deep Learning methods to object recognition. I also work on low-level vision problems, with applications to computational photography and astronomy.
Deep Learning for Computer Vision
NIPS 2013 Tutorial
Slides Coming soon....
Online Recognition Demo
See our deep convolutional network demo here. This network achieves 16.5% top-5 error on the Imagenet 2012 classification challenge, around 2% better than the network of Krizhevsky et al. (NIPS 2012).
International Conference on Learning Representations 2014
I am one of the Program Chairs for a new conference on feature learning and representation learning. The submission deadline is December 20th. Check out the website.
Visualizing and Understanding Convolutional Networks
Matt Zeiler and Rob Fergus,
arXiv pre-print, Nov 2013, PDF
Reconnaissance of the HR 8799 Exosolar System I: Near IR Spectroscopy
Indoor Segmentation and Support Inference from RGBD Images
Adaptive Deconvolutional Networks for Mid and High Level Feature Learning
Learning Invarance through Imitation
Blind Deconvolution using a Normalized Sparsity Measure
Dark Flash Photography
80 million tiny images: a large dataset for non-parametric object and scene recognition